We compare the performance of the adaptive precision ADMM with different
sub-problem solvers to JOPT(BO) (BO on the joint problem with all the variables)
on 8 OpenML [1]} binary classification data sets where we
optimize for the area under the ROC curve on 10% of the data as the validation
set. Both adaptive precision ADMM and JOPT(BO) are executed for 1 hour with 10
repetitions, and the incumbent objective is averaged over the 10
repetitions. All evaluations were run single-threaded on a 8 core 8GB CentOS
virtual machines. For each data set, we note the objective \(f_J\)  achieved by
JOPT(BO) at the end of 1 hour and note the time \(T_A\)  (in seconds) required by
adaptive precision ADMM to reach that objective. Then we also note the best
objective achieved by ADMM at the end of 1 hour \(f_A\) . The speedup \(S\)  for ADMM
is computed as \(S = \frac{3600}{T_A}\)  and the improvement (in %) \(I = 100 *\frac{f_J - f_A}{f_J}\) .
